# Semiotic Complexity and Its Epistemological Implications for Modeling Culture

arXiv: https://arxiv.org/abs/2508.00095 · HTML: https://mlsanity.com/paper/2508.00095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale](https://mlsanity.com/paper/2505.23785/) (2025)
- [On Classification with Large Language Models in Cultural Analytics](https://mlsanity.com/paper/2410.12029/) (2024)
- [Synthesizing Proteins on the Graphics Card. Protein Folding and the Limits of Critical AI Studies](https://mlsanity.com/paper/2405.09788/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Differential Meaning of Models: A Framework for Analyzing the Structural Consequences of Semantic Modeling Decisions](https://mlsanity.com/paper/2509.00248/) (2025)

## Abstract (arXiv)

Greater theorizing of methods in the computational humanities is needed for epistemological and interpretive clarity, and therefore the maturation of the field. In this paper, we frame such modeling work as engaging in translation work from a cultural, linguistic domain into a computational, mathematical domain, and back again. Translators benefit from articulating the theory of their translation process, and so do computational humanists in their work -- to ensure internal consistency, avoid subtle yet consequential translation errors, and facilitate interpretive transparency. Our contribution in this paper is to lay out a particularly consequential dimension of the lack of theorizing and the sorts of translation errors that emerge in our modeling practices as a result. Along these lines we introduce the idea of semiotic complexity as the degree to which the meaning of some text may vary across interpretive lenses, and make the case that dominant modeling practices -- especially around evaluation -- commit a translation error by treating semiotically complex data as semiotically simple when it seems epistemologically convenient by conferring superficial clarity. We then lay out several recommendations for researchers to better account for these epistemological issues in their own work.